ARCHITECTURES FOR GENERALITY & AUTONOMY

August, 19, 2017

Reinterpreting
The Cortical Circuit
Hiroshi Yamakawa* Naoya Arakawa* Koichi Takahashi*

npo
The Whole Brain Architecture Initiative， a speciﬁed non-proﬁt organization

http://wba-initiative.org/

Uniformity of the neocortex is the basis for human versa4lity
Canonical
cortical circuit

•••
•
•
•
•

Bodily-kinesthetic
Verbal-linguistic
Logical-mathematical
Musical-rhythmic andharmonic
Interpersonal
Intrapersonal
Visual-spatialc.f. ： http://bio1152.nicerweb.com/Locked/media/
ch48/48_27HumanCerebralCortex.jpg

Various functions are realized on an
anatomically uniform structureー＞ general intelligence
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ARCHITECTURES FOR GENERALITY & AUTONOMYReinterpre4ng the neocortex: why now?
• Importance:
The versa4lity of human intelligence comes from theuniform local circuit of the neocortex.
• Feasibility:
Recent progress in neuroscience and machinelearning is making this reinterpreta4on more likely• Aim:
To suggest a domain model that machine learning
experts can understand and invite them tocontribute to AGI research.
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ARCHITECTURES FOR GENERALITY & AUTONOMYCan brain func4ons be implemented on ANNs?
① Canonical cor4cal circuits are gradually being unveiled② Some cogni4ve func4ons are being implemented onar4ﬁcial neural networks (ANNs)③ ① & ② can be combined①③ Homology of CNN and neocortex for object recognition function

Kenneth D. Harris & Thomas D. Mrsic-Flogel, Corticalconnectivity and sensory coding, Nature 503, 51–58, 2013

Yamins and DiCarlo, Nat Neurosci.19:356-65, 2016
4ARCHITECTURES FOR GENERALITY & AUTONOMYA rough sketch of the en4re brain architecture
Reinforcement
learningBasalganglia

Hippocampus

HigherIntegra4ng
sequen4al
cogni4onbottom-up
top-down
controlcanonical corticalcircuit modelEC

M2
強化学習

Lower

M1
muscleS1
tac4leV1visionaudio

Environment
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ARCHITECTURES FOR GENERALITY & AUTONOMYReinterpreta4on of the cor4cal circuits
Input 1Higher L6(Basal gangliacontrolled)
ThalamusHigher L2/3,
Hippocampus,
Cerebellum

Neocortex

Signal Semantics

L1

Output 1

Signal Semantics

Control(A)en5on,context, etc.)
Input 2
Hidden state
outputcontrol

Activity control

Input

L6

L5

Hidden state[Ac5on output]

Lower L2/3

Input 5(Lower L5 origin)
ThalamusHidden sate(bo)om up)

Basal ganglia,[Pyramidal tracts]

Output 3

L2/3

State

Input 4State(bo)om up)

Lower L6,
Lower L1

Output 2

Input 3

State(top down)Control(A)en5on,context, etc.)

OutputLower L2/3
Higher
L2/3 or L4,
Hippocampus

L4
Information selection(attention)

ThalamusSignals related to the outside world: Represent beliefs andpredictions.(State = latest state, Hidden state = include history)
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Other control
related signalsARCHITECTURES FOR GENERALITY & AUTONOMYBocom-up direct paths used by CNN
Input 1Higher L6(Basal gangliacontrolled)
ThalamusHigher L2/3,
Hippocampus,
Cerebellum

Neocortex

Signal Semantics

L1

Output 1

Signal Semantics

Control(A)en5on,context, etc.)
Input 2
Hidden state
outputcontrol

Activity control

Input

L6

L5

Hidden state[Ac5on output]

Lower L2/3

Input 5(Lower L5 origin)
ThalamusHidden sate(bo)om up)

Basal ganglia,[Pyramidal tracts]

Output 3

L2/3

Input 4State(bo)om up)

Lower L6,
Lower L1

Output 2

Input 3

State(top down)Control(A)en5on,
context, etc.)

Output

L4
Information selection(attention)

State

Lower L2/3
HigherL2/3 or L4,
Hippocampus
Thalamus7

ARCHITECTURES FOR GENERALITY & AUTONOMY

Major pathways• bocom-up direct path ← already described• bocom-up indirect path
• top-down direct path #1• top-down direct path #2• top-down direct path #3

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ARCHITECTURES FOR GENERALITY & AUTONOMY

Deﬁning the canonical cor4cal circuit framework• Standard I / O seman4cs of neocor4cal local circuits– Integrate neuroscien4ﬁc ﬁndings• Output is organized by layer• Input is organized from the nature of source– Seman4cs understandable by machinelearning specialists• Func4on of neocor4cal local circuits– Func4ons of intelligent agents to be carried out byneocortex• Func4ons suﬀered by damage of neocortex• Func4ons that cannot bear by other brain organs– To be consistent with exis4ng neocor4cal computa4onal
models• E.g. visual object recogni4on

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ARCHITECTURES FOR GENERALITY & AUTONOMYReferred models and descrip4ons

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ARCHITECTURES FOR GENERALITY & AUTONOMYOverview
Various artificial
neural network models

Functions

I/Os

Neuroscientificknowledge

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ARCHITECTURES FOR GENERALITY & AUTONOMY

Referring various model• Project AGI [Kowadlo 15, Kowadlo 16] was arela4vely comprehensive descrip4on– It was consistent with many other models– We had to adjust the input from the thalamus slightly• The Bayesian ﬁlter has low coverage but
conserva4ve• In Kawaguchi hypothesis, the descrip4on of L5
was detailed and consistent with others– There exist feed-back loops between L2 / 3 and L5• Most of these were consistent with theneuroscience ﬁndings of Cogni4ve consilience
12

ARCHITECTURES FOR GENERALITY & AUTONOMYOverview
Various artificial
neural network models

Functions

I/Os

Neuroscientificknowledge

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ARCHITECTURES FOR GENERALITY & AUTONOMY

Whole Brain Architecture approach @ WBAI npo‘to create a human-like AGI by learning fromthe architecture of the en8re brain’
AINeocortex
Basal Ganglia

Amygdala
Hippocampus(1) Develop machinelearning modules for partsof the brain(2) Integrate the modules tocreate cogni<vearchitectureIntelligent Agent

Neocortex
Executive

Hippo- Intention
campus
GoalStateTransition

Utility

BGStateState

Recognition

StateUtility
Evaluation
RewardAmygdala
RewardGeneration
Perception

ActionEnvironment

To reach AGI, we start from mimicking highlevel architectures of the brain, and gradually
introduce details as needed
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ARCHITECTURES FOR GENERALITY & AUTONOMYOpen plamorm strategy @ WBAI npo
NPO WBAI is making a place for collaborative developments
Integration：

Catch-up：

A brain-inspiredarchitecture platform for
integrating machinelearning modules

An open technician communitythat can implement thecutting-edge machine leanings
in a short term

Neuroscientiﬁc
Data

Virtualexperiments

ConnectomeActuators

Sensors

Integration

Intro.

Whole-brain
Connectomic
Architecture

NeuralActivity

Machinelearning
repositoryML
modulesCognitivearchitecture

Catch導入 upll

Behavioral
Data

Integrated Executive platform
Middleware for managing computational resources for
executing and learning multiple modules in parallelex.）BriCAcore , ROS, Flint, Nengo,

15

arXiv
GithubBriCA Language
An architecture description language that
describes connections of machine learning
modules independent of computer environment

Artiﬁcial neuralnetwork,
graphical modell

Validation
Reimplement
ation
Improvement

ARCHITECTURES FOR GENERALITY & AUTONOMYOpen Plamorm Strategy @WBAI npo

Let’s build a brain, together

To “harmonize AGI with human beings” bydemocratizing AI technologies( c.f. “Common goods”:
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Asiloma AI Principle 23 )ARCHITECTURES FOR GENERALITY & AUTONOMYStandardiza4on trend in progress
One Model To Learn Them All(Google, 16 Jun 2017)

KEY: (1) Convolutions. (2) Attention(3) Sparsely-gated mixture-of-experts

TASKS:(1) WSJ speech corpus(2) ImageNet dataset(3) COCO image captioning dataset(4) WSJ parsing dataset(5) WMT English-German translation corpus(6) The reverse of the above: German-English translation.
(7) WMT English-French translation corpus(8) The reverse of the above: German-French translation.

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ARCHITECTURES FOR GENERALITY & AUTONOMYOur message
ü Definition and implementation of the canonical neocorticalcircuit is an important landmark for brain-inspired AGI.
ü We proposed a framework that can be understandable byML experts while maintaining neurobiological accuracy.
ü We believe that the brain-inspired approach will harmonize
AGI with mankind, and established a non-profit organization
to develop an open-platform for co-creation of brain-inspired
AGI.
ü If you are interested in our research and activities, pleasecontact us.
Posi4ons available: Posdocs/Students

hcp://wba-ini4a4ve.org/
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ARCHITECTURES FOR GENERALITY & AUTONOMYWe promote open co-crea4on of AGI on Brain
Research organizations

The University ofElectoro-Communications
AIST AIRC

Keio University

Tamagawa University

Promote R&D
Human resource
development• Hackathon• Seminar• SchoolConstruction of thedevelopment environment• World simulator• Evaluation of AGI• Platform for integration(BriCA) Poster Takahashi• Grounding to brain

SupportsToshio Okubo
Kentro Goto
Ren Sakaki
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ARCHITECTURES FOR GENERALITY & AUTONOMY